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Author Fernandez, C.; Valle, C.; Saravia, F.; Allende, H. pdf  doi
openurl 
  Title Behavior analysis of neural network ensemble algorithm on a virtual machine cluster Type
  Year 2012 Publication Neural Computing & Applications Abbreviated Journal Neural Comput. Appl.  
  Volume 21 Issue 3 Pages 535-542  
  Keywords Ensemble learning; Artificial neural networks; Virtualization; Multicore processor; Parallel algorithms  
  Abstract Ensemble learning has gained considerable attention in different learning tasks including regression, classification, and clustering problems. One of the drawbacks of the ensemble is the high computational cost of training stages. Resampling local negative correlation (RLNC) is a technique that combines two well-known methods to generate ensemble diversity-resampling and error negative correlation-and a fine-grain parallel approach that allows us to achieve a satisfactory balance between accuracy and efficiency. In this paper, we introduce a structure of the virtual machine aimed to test diverse selection strategies of parameters in neural ensemble designs, such as RLNC. We assess the parallel performance of this approach on a virtual machine cluster based on the full virtualization paradigm, using speedup and efficiency as performance metrics, for different numbers of processors and training data sizes.  
  Address [Fernandez, Cesar; Valle, Carlos; Saravia, Francisco; Allende, Hector] Univ Tecn Federico Santa Maria, Dept Comp Sci, Valparaiso 110 V, Chile, Email: cesferna@inf.utfsm.cl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0941-0643 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000301578900014 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 251  
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Author Goles, E.; Montealegre, P. doi  openurl
  Title The complexity of the asynchronous prediction of the majority automata Type
  Year 2020 Publication Information and Computation Abbreviated Journal Inf. Comput.  
  Volume 274 Issue SI Pages  
  Keywords Majority automata; Cellular automata; Prediction problem; Asynchronous updating; Computational complexity; Parallel algorithms; Bootstrap percolation; NP-Completeness  
  Abstract We consider the asynchronous prediction problem for some automaton as the one consisting in determining, given an initial configuration, if there exists a non-zero probability that some selected site changes its state, when the network is updated picking one site at a time uniformly at random. We show that for the majority automaton, the asynchronous prediction problem is in NC in the two-dimensional lattice with von Neumann neighborhood. Later, we show that in three or more dimensions the problem is NP-Complete.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0890-5401 ISBN Medium  
  Area Expedition Conference  
  Notes Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1124  
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Author Goles, E.; Maldonado, D.; Montealegre, P.; Ollinger, N. doi  openurl
  Title On the complexity of the stability problem of binary freezing totalistic cellular automata Type
  Year 2020 Publication Information And Computation Abbreviated Journal Inf. Comput.  
  Volume 274 Issue Pages 21 pp  
  Keywords Cellular automata; Computational complexity; Freezing cellular automata; Totalistic cellular automata; Fast parallel algorithms; P-Complete  
  Abstract In this paper we study the family of two-state Totalistic Freezing Cellular Automata (TFCA) defined over the triangular and square grids with von Neumann neighborhoods. We say that a Cellular Automaton is Freezing and Totalistic if the active cells remain unchanged, and the new value of an inactive cell depends only on the sum of its active neighbors. We classify all the Cellular Automata in the class of TFCA, grouping them in five different classes: the Trivial rules, Turing Universal rules, Algebraic rules, Topological rules and Fractal Growing rules. At the same time, we study in this family the STABILITY problem, consisting in deciding whether an inactive cell becomes active, given an initial configuration. We exploit the properties of the automata in each group to show that: For Algebraic and Topological Rules the STABILITY problem is in NC. For Turing Universal rules the STABILITY problem is P-Complete. (C) 2020 Elsevier Inc. All rights reserved.  
  Address [Goles, Eric; Montealegre, Pedro] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: eric.chacc@uai.cl;  
  Corporate Author Thesis  
  Publisher Academic Press Inc Elsevier Science Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0890-5401 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000573267700008 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1238  
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